Overview

Dataset statistics

Number of variables27
Number of observations437
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory92.3 KiB
Average record size in memory216.3 B

Variable types

Numeric20
Categorical7

Alerts

Sl. No. is highly overall correlated with NT and 12 other fieldsHigh correlation
NT is highly overall correlated with Sl. No. and 12 other fieldsHigh correlation
Ct is highly overall correlated with Sl. No. and 12 other fieldsHigh correlation
DT is highly overall correlated with Sl. No. and 13 other fieldsHigh correlation
Dt is highly overall correlated with Sl. No. and 12 other fieldsHigh correlation
TT is highly overall correlated with Ct and 9 other fieldsHigh correlation
C is highly overall correlated with NT and 10 other fieldsHigh correlation
Ni is highly overall correlated with Sl. No. and 4 other fieldsHigh correlation
Cr is highly overall correlated with Mo and 7 other fieldsHigh correlation
Cu is highly overall correlated with NiHigh correlation
Mo is highly overall correlated with Cr and 1 other fieldsHigh correlation
RedRatio is highly overall correlated with NiHigh correlation
Fatigue is highly overall correlated with Sl. No. and 13 other fieldsHigh correlation
THT is highly overall correlated with Sl. No. and 11 other fieldsHigh correlation
THt is highly overall correlated with Sl. No. and 14 other fieldsHigh correlation
THQCr is highly overall correlated with Sl. No. and 14 other fieldsHigh correlation
CT is highly overall correlated with Sl. No. and 15 other fieldsHigh correlation
QmT is highly overall correlated with Sl. No. and 13 other fieldsHigh correlation
Tt is highly overall correlated with Sl. No. and 14 other fieldsHigh correlation
TCr is highly overall correlated with Sl. No. and 14 other fieldsHigh correlation
CT is highly imbalanced (50.1%)Imbalance
QmT is highly imbalanced (62.9%)Imbalance
Tt is highly imbalanced (58.1%)Imbalance
TCr is highly imbalanced (58.1%)Imbalance
Sl. No. is uniformly distributedUniform
Sl. No. has unique valuesUnique
Ct has 389 (89.0%) zerosZeros
Dt has 389 (89.0%) zerosZeros
Mo has 250 (57.2%) zerosZeros
dA has 21 (4.8%) zerosZeros
dB has 341 (78.0%) zerosZeros
dC has 219 (50.1%) zerosZeros

Reproduction

Analysis started2023-11-17 18:41:33.154681
Analysis finished2023-11-17 18:43:47.687802
Duration2 minutes and 14.53 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Sl. No.
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct437
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219
Minimum1
Maximum437
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-11-18T00:13:48.044845image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile22.8
Q1110
median219
Q3328
95-th percentile415.2
Maximum437
Range436
Interquartile range (IQR)218

Descriptive statistics

Standard deviation126.29529
Coefficient of variation (CV)0.57669082
Kurtosis-1.2
Mean219
Median Absolute Deviation (MAD)109
Skewness0
Sum95703
Variance15950.5
MonotonicityStrictly increasing
2023-11-18T00:13:48.388926image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
289 1
 
0.2%
300 1
 
0.2%
299 1
 
0.2%
298 1
 
0.2%
297 1
 
0.2%
296 1
 
0.2%
295 1
 
0.2%
294 1
 
0.2%
293 1
 
0.2%
Other values (427) 427
97.7%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
437 1
0.2%
436 1
0.2%
435 1
0.2%
434 1
0.2%
433 1
0.2%
432 1
0.2%
431 1
0.2%
430 1
0.2%
429 1
0.2%
428 1
0.2%

NT
Real number (ℝ)

Distinct7
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean872.29977
Minimum825
Maximum930
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-11-18T00:13:48.727025image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum825
5-th percentile825
Q1865
median870
Q3870
95-th percentile930
Maximum930
Range105
Interquartile range (IQR)5

Descriptive statistics

Standard deviation26.212073
Coefficient of variation (CV)0.030049387
Kurtosis0.64218999
Mean872.29977
Median Absolute Deviation (MAD)0
Skewness0.68249171
Sum381195
Variance687.07278
MonotonicityNot monotonic
2023-11-18T00:13:49.055145image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
870 228
52.2%
845 51
 
11.7%
930 48
 
11.0%
865 36
 
8.2%
825 33
 
7.6%
900 30
 
6.9%
885 11
 
2.5%
ValueCountFrequency (%)
825 33
 
7.6%
845 51
 
11.7%
865 36
 
8.2%
870 228
52.2%
885 11
 
2.5%
900 30
 
6.9%
930 48
 
11.0%
ValueCountFrequency (%)
930 48
 
11.0%
900 30
 
6.9%
885 11
 
2.5%
870 228
52.2%
865 36
 
8.2%
845 51
 
11.7%
825 33
 
7.6%

THT
Categorical

Distinct5
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size25.7 KiB
845
198 
855
111 
30
59 
865
36 
825
33 

Length

Max length3
Median length3
Mean length2.8649886
Min length2

Characters and Unicode

Total characters1252
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30
2nd row30
3rd row30
4th row30
5th row30

Common Values

ValueCountFrequency (%)
845 198
45.3%
855 111
25.4%
30 59
 
13.5%
865 36
 
8.2%
825 33
 
7.6%

Length

2023-11-18T00:13:49.447097image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-18T00:13:49.980671image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
845 198
45.3%
855 111
25.4%
30 59
 
13.5%
865 36
 
8.2%
825 33
 
7.6%

Most occurring characters

ValueCountFrequency (%)
5 489
39.1%
8 378
30.2%
4 198
15.8%
3 59
 
4.7%
0 59
 
4.7%
6 36
 
2.9%
2 33
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1252
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 489
39.1%
8 378
30.2%
4 198
15.8%
3 59
 
4.7%
0 59
 
4.7%
6 36
 
2.9%
2 33
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1252
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 489
39.1%
8 378
30.2%
4 198
15.8%
3 59
 
4.7%
0 59
 
4.7%
6 36
 
2.9%
2 33
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 489
39.1%
8 378
30.2%
4 198
15.8%
3 59
 
4.7%
0 59
 
4.7%
6 36
 
2.9%
2 33
 
2.6%

THt
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size25.2 KiB
30
378 
0
59 

Length

Max length2
Median length2
Mean length1.8649886
Min length1

Characters and Unicode

Total characters815
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
30 378
86.5%
0 59
 
13.5%

Length

2023-11-18T00:13:50.327745image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-18T00:13:50.713709image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
30 378
86.5%
0 59
 
13.5%

Most occurring characters

ValueCountFrequency (%)
0 437
53.6%
3 378
46.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 815
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 437
53.6%
3 378
46.4%

Most occurring scripts

ValueCountFrequency (%)
Common 815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 437
53.6%
3 378
46.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 437
53.6%
3 378
46.4%

THQCr
Categorical

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size25.0 KiB
8
276 
24
102 
0
59 

Length

Max length2
Median length1
Mean length1.2334096
Min length1

Characters and Unicode

Total characters539
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
8 276
63.2%
24 102
 
23.3%
0 59
 
13.5%

Length

2023-11-18T00:13:51.010915image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-18T00:13:51.453730image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
8 276
63.2%
24 102
 
23.3%
0 59
 
13.5%

Most occurring characters

ValueCountFrequency (%)
8 276
51.2%
2 102
 
18.9%
4 102
 
18.9%
0 59
 
10.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 539
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 276
51.2%
2 102
 
18.9%
4 102
 
18.9%
0 59
 
10.9%

Most occurring scripts

ValueCountFrequency (%)
Common 539
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 276
51.2%
2 102
 
18.9%
4 102
 
18.9%
0 59
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 539
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 276
51.2%
2 102
 
18.9%
4 102
 
18.9%
0 59
 
10.9%

CT
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
30
389 
930
48 

Length

Max length3
Median length2
Mean length2.1098398
Min length2

Characters and Unicode

Total characters922
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30
2nd row30
3rd row30
4th row30
5th row30

Common Values

ValueCountFrequency (%)
30 389
89.0%
930 48
 
11.0%

Length

2023-11-18T00:13:51.790831image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-18T00:13:52.209708image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
30 389
89.0%
930 48
 
11.0%

Most occurring characters

ValueCountFrequency (%)
3 437
47.4%
0 437
47.4%
9 48
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 922
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 437
47.4%
0 437
47.4%
9 48
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Common 922
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 437
47.4%
0 437
47.4%
9 48
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 437
47.4%
0 437
47.4%
9 48
 
5.2%

Ct
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.502059
Minimum0
Maximum540
Zeros389
Zeros (%)89.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-11-18T00:13:52.498940image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile450
Maximum540
Range540
Interquartile range (IQR)0

Descriptive statistics

Standard deviation126.9247
Coefficient of variation (CV)3.1337838
Kurtosis7.8941134
Mean40.502059
Median Absolute Deviation (MAD)0
Skewness3.0722131
Sum17699.4
Variance16109.879
MonotonicityNot monotonic
2023-11-18T00:13:52.841021image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 389
89.0%
340.2 9
 
2.1%
469.8 6
 
1.4%
450 6
 
1.4%
499.8 6
 
1.4%
139.8 3
 
0.7%
540 3
 
0.7%
130.2 3
 
0.7%
510 3
 
0.7%
100.2 3
 
0.7%
Other values (2) 6
 
1.4%
ValueCountFrequency (%)
0 389
89.0%
90 3
 
0.7%
100.2 3
 
0.7%
130.2 3
 
0.7%
139.8 3
 
0.7%
340.2 9
 
2.1%
450 6
 
1.4%
469.8 6
 
1.4%
499.8 6
 
1.4%
510 3
 
0.7%
ValueCountFrequency (%)
540 3
 
0.7%
529.8 3
 
0.7%
510 3
 
0.7%
499.8 6
1.4%
469.8 6
1.4%
450 6
1.4%
340.2 9
2.1%
139.8 3
 
0.7%
130.2 3
 
0.7%
100.2 3
 
0.7%

DT
Real number (ℝ)

Distinct7
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.69984
Minimum30
Maximum903.333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-11-18T00:13:53.115287image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile30
Q130
median30
Q330
95-th percentile895.517
Maximum903.333
Range873.333
Interquartile range (IQR)0

Descriptive statistics

Standard deviation267.12893
Coefficient of variation (CV)2.1594929
Kurtosis4.3104404
Mean123.69984
Median Absolute Deviation (MAD)0
Skewness2.5069574
Sum54056.832
Variance71357.867
MonotonicityNot monotonic
2023-11-18T00:13:53.410502image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
30 389
89.0%
895.812 18
 
4.1%
850 12
 
2.7%
895.517 9
 
2.1%
882.381 3
 
0.7%
903.333 3
 
0.7%
881.807 3
 
0.7%
ValueCountFrequency (%)
30 389
89.0%
850 12
 
2.7%
881.807 3
 
0.7%
882.381 3
 
0.7%
895.517 9
 
2.1%
895.812 18
 
4.1%
903.333 3
 
0.7%
ValueCountFrequency (%)
903.333 3
 
0.7%
895.812 18
 
4.1%
895.517 9
 
2.1%
882.381 3
 
0.7%
881.807 3
 
0.7%
850 12
 
2.7%
30 389
89.0%

Dt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8439359
Minimum0
Maximum70.2
Zeros389
Zeros (%)89.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-11-18T00:13:53.658835image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile45
Maximum70.2
Range70.2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation15.700076
Coefficient of variation (CV)3.2411815
Kurtosis10.625899
Mean4.8439359
Median Absolute Deviation (MAD)0
Skewness3.4100043
Sum2116.8
Variance246.49238
MonotonicityNot monotonic
2023-11-18T00:13:53.960029image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 389
89.0%
70.2 18
 
4.1%
15 12
 
2.7%
34.8 9
 
2.1%
25.2 3
 
0.7%
45 3
 
0.7%
49.8 3
 
0.7%
ValueCountFrequency (%)
0 389
89.0%
15 12
 
2.7%
25.2 3
 
0.7%
34.8 9
 
2.1%
45 3
 
0.7%
49.8 3
 
0.7%
70.2 18
 
4.1%
ValueCountFrequency (%)
70.2 18
 
4.1%
49.8 3
 
0.7%
45 3
 
0.7%
34.8 9
 
2.1%
25.2 3
 
0.7%
15 12
 
2.7%
0 389
89.0%

QmT
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size25.3 KiB
30
389 
60
 
36
140
 
12

Length

Max length3
Median length2
Mean length2.02746
Min length2

Characters and Unicode

Total characters886
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30
2nd row30
3rd row30
4th row30
5th row30

Common Values

ValueCountFrequency (%)
30 389
89.0%
60 36
 
8.2%
140 12
 
2.7%

Length

2023-11-18T00:13:54.264257image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-18T00:13:54.569398image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
30 389
89.0%
60 36
 
8.2%
140 12
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 437
49.3%
3 389
43.9%
6 36
 
4.1%
1 12
 
1.4%
4 12
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 886
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 437
49.3%
3 389
43.9%
6 36
 
4.1%
1 12
 
1.4%
4 12
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 886
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 437
49.3%
3 389
43.9%
6 36
 
4.1%
1 12
 
1.4%
4 12
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 886
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 437
49.3%
3 389
43.9%
6 36
 
4.1%
1 12
 
1.4%
4 12
 
1.4%

TT
Real number (ℝ)

Distinct13
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean536.84211
Minimum30
Maximum680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-11-18T00:13:54.756900image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile160
Q1550
median600
Q3650
95-th percentile650
Maximum680
Range650
Interquartile range (IQR)100

Descriptive statistics

Standard deviation164.10196
Coefficient of variation (CV)0.30568013
Kurtosis2.2242753
Mean536.84211
Median Absolute Deviation (MAD)50
Skewness-1.8683266
Sum234600
Variance26929.454
MonotonicityNot monotonic
2023-11-18T00:13:55.093996image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
550 100
22.9%
600 100
22.9%
650 100
22.9%
160 36
 
8.2%
580 20
 
4.6%
630 20
 
4.6%
680 20
 
4.6%
200 12
 
2.7%
30 11
 
2.5%
430 6
 
1.4%
Other values (3) 12
 
2.7%
ValueCountFrequency (%)
30 11
 
2.5%
160 36
 
8.2%
200 12
 
2.7%
420 3
 
0.7%
430 6
 
1.4%
490 3
 
0.7%
500 6
 
1.4%
550 100
22.9%
580 20
 
4.6%
600 100
22.9%
ValueCountFrequency (%)
680 20
 
4.6%
650 100
22.9%
630 20
 
4.6%
600 100
22.9%
580 20
 
4.6%
550 100
22.9%
500 6
 
1.4%
490 3
 
0.7%
430 6
 
1.4%
420 3
 
0.7%

Tt
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size25.3 KiB
60
378 
120
48 
0
 
11

Length

Max length3
Median length2
Mean length2.0846682
Min length1

Characters and Unicode

Total characters911
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
60 378
86.5%
120 48
 
11.0%
0 11
 
2.5%

Length

2023-11-18T00:13:55.457067image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-18T00:13:55.765202image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
60 378
86.5%
120 48
 
11.0%
0 11
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 437
48.0%
6 378
41.5%
1 48
 
5.3%
2 48
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 911
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 437
48.0%
6 378
41.5%
1 48
 
5.3%
2 48
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common 911
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 437
48.0%
6 378
41.5%
1 48
 
5.3%
2 48
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 911
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 437
48.0%
6 378
41.5%
1 48
 
5.3%
2 48
 
5.3%

TCr
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size26.1 KiB
24.0
378 
0.5
48 
0.0
 
11

Length

Max length4
Median length4
Mean length3.8649886
Min length3

Characters and Unicode

Total characters1689
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
24.0 378
86.5%
0.5 48
 
11.0%
0.0 11
 
2.5%

Length

2023-11-18T00:13:55.995631image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-18T00:13:56.239933image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
24.0 378
86.5%
0.5 48
 
11.0%
0.0 11
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 448
26.5%
. 437
25.9%
2 378
22.4%
4 378
22.4%
5 48
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1252
74.1%
Other Punctuation 437
 
25.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 448
35.8%
2 378
30.2%
4 378
30.2%
5 48
 
3.8%
Other Punctuation
ValueCountFrequency (%)
. 437
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1689
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 448
26.5%
. 437
25.9%
2 378
22.4%
4 378
22.4%
5 48
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1689
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 448
26.5%
. 437
25.9%
2 378
22.4%
4 378
22.4%
5 48
 
2.8%

C
Real number (ℝ)

Distinct42
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38839817
Minimum0.17
Maximum0.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-11-18T00:13:56.530192image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.17
5-th percentile0.21
Q10.34
median0.4
Q30.43
95-th percentile0.56
Maximum0.63
Range0.46
Interquartile range (IQR)0.09

Descriptive statistics

Standard deviation0.096363688
Coefficient of variation (CV)0.24810541
Kurtosis0.22473971
Mean0.38839817
Median Absolute Deviation (MAD)0.05
Skewness-0.13174968
Sum169.73
Variance0.0092859604
MonotonicityNot monotonic
2023-11-18T00:13:56.787509image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0.4 45
 
10.3%
0.42 42
 
9.6%
0.35 24
 
5.5%
0.41 24
 
5.5%
0.38 21
 
4.8%
0.39 18
 
4.1%
0.43 18
 
4.1%
0.32 18
 
4.1%
0.37 18
 
4.1%
0.45 18
 
4.1%
Other values (32) 191
43.7%
ValueCountFrequency (%)
0.17 4
 
0.9%
0.18 8
1.8%
0.19 4
 
0.9%
0.2 4
 
0.9%
0.21 16
3.7%
0.22 2
 
0.5%
0.23 13
3.0%
0.25 1
 
0.2%
0.26 4
 
0.9%
0.27 2
 
0.5%
ValueCountFrequency (%)
0.63 2
 
0.5%
0.62 2
 
0.5%
0.61 4
 
0.9%
0.6 2
 
0.5%
0.59 2
 
0.5%
0.57 6
1.4%
0.56 6
1.4%
0.55 3
 
0.7%
0.54 10
2.3%
0.53 8
1.8%

Si
Real number (ℝ)

Distinct27
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2995881
Minimum0.16
Maximum2.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-11-18T00:13:57.103623image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.16
5-th percentile0.2
Q10.24
median0.26
Q30.29
95-th percentile0.34
Maximum2.05
Range1.89
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.24604451
Coefficient of variation (CV)0.82127599
Kurtosis35.498863
Mean0.2995881
Median Absolute Deviation (MAD)0.03
Skewness5.9454033
Sum130.92
Variance0.060537903
MonotonicityNot monotonic
2023-11-18T00:13:57.332052image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.26 70
16.0%
0.25 68
15.6%
0.29 43
9.8%
0.3 34
7.8%
0.27 32
7.3%
0.24 30
6.9%
0.22 28
 
6.4%
0.21 26
 
5.9%
0.23 20
 
4.6%
0.28 15
 
3.4%
Other values (17) 71
16.2%
ValueCountFrequency (%)
0.16 3
 
0.7%
0.17 2
 
0.5%
0.18 1
 
0.2%
0.19 7
 
1.6%
0.2 10
 
2.3%
0.21 26
 
5.9%
0.22 28
6.4%
0.23 20
 
4.6%
0.24 30
6.9%
0.25 68
15.6%
ValueCountFrequency (%)
2.05 2
 
0.5%
1.99 2
 
0.5%
1.96 2
 
0.5%
1.51 1
 
0.2%
1.5 2
 
0.5%
1.49 1
 
0.2%
1.31 2
 
0.5%
0.35 7
1.6%
0.34 6
1.4%
0.33 6
1.4%

Mn
Real number (ℝ)

Distinct55
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.82302059
Minimum0.37
Maximum1.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-11-18T00:13:57.597303image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.37
5-th percentile0.51
Q10.7
median0.76
Q30.8
95-th percentile1.52
Maximum1.6
Range1.23
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.27902769
Coefficient of variation (CV)0.33902881
Kurtosis2.1330979
Mean0.82302059
Median Absolute Deviation (MAD)0.05
Skewness1.7221041
Sum359.66
Variance0.077856451
MonotonicityNot monotonic
2023-11-18T00:13:57.945415image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.74 33
 
7.6%
0.7 33
 
7.6%
0.76 29
 
6.6%
0.77 28
 
6.4%
0.79 22
 
5.0%
0.71 21
 
4.8%
0.8 19
 
4.3%
0.75 17
 
3.9%
0.72 15
 
3.4%
0.78 15
 
3.4%
Other values (45) 205
46.9%
ValueCountFrequency (%)
0.37 4
0.9%
0.42 1
 
0.2%
0.43 1
 
0.2%
0.44 2
 
0.5%
0.45 1
 
0.2%
0.47 1
 
0.2%
0.49 6
1.4%
0.5 1
 
0.2%
0.51 6
1.4%
0.52 3
0.7%
ValueCountFrequency (%)
1.6 3
 
0.7%
1.59 3
 
0.7%
1.58 9
2.1%
1.57 3
 
0.7%
1.56 3
 
0.7%
1.52 3
 
0.7%
1.51 6
1.4%
1.5 3
 
0.7%
1.49 3
 
0.7%
1.48 3
 
0.7%

P
Real number (ℝ)

Distinct24
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.015784897
Minimum0.002
Maximum0.031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-11-18T00:13:58.182774image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.002
5-th percentile0.009
Q10.012
median0.016
Q30.019
95-th percentile0.026
Maximum0.031
Range0.029
Interquartile range (IQR)0.007

Descriptive statistics

Standard deviation0.0052053652
Coefficient of variation (CV)0.32976871
Kurtosis-0.0075781415
Mean0.015784897
Median Absolute Deviation (MAD)0.004
Skewness0.34430074
Sum6.898
Variance2.7095826 × 10-5
MonotonicityNot monotonic
2023-11-18T00:13:58.393197image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0.017 41
 
9.4%
0.015 35
 
8.0%
0.019 33
 
7.6%
0.01 33
 
7.6%
0.016 31
 
7.1%
0.012 31
 
7.1%
0.014 28
 
6.4%
0.011 26
 
5.9%
0.013 24
 
5.5%
0.018 22
 
5.0%
Other values (14) 133
30.4%
ValueCountFrequency (%)
0.002 4
 
0.9%
0.004 1
 
0.2%
0.007 4
 
0.9%
0.008 12
 
2.7%
0.009 20
4.6%
0.01 33
7.6%
0.011 26
5.9%
0.012 31
7.1%
0.013 24
5.5%
0.014 28
6.4%
ValueCountFrequency (%)
0.031 3
 
0.7%
0.029 1
 
0.2%
0.027 11
 
2.5%
0.026 12
 
2.7%
0.024 16
3.7%
0.023 3
 
0.7%
0.022 12
 
2.7%
0.021 21
4.8%
0.02 13
 
3.0%
0.019 33
7.6%

S
Real number (ℝ)

Distinct26
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.014610984
Minimum0.003
Maximum0.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-11-18T00:13:58.685430image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.003
5-th percentile0.005
Q10.01
median0.015
Q30.019
95-th percentile0.025
Maximum0.03
Range0.027
Interquartile range (IQR)0.009

Descriptive statistics

Standard deviation0.0061453845
Coefficient of variation (CV)0.42060032
Kurtosis-0.68720229
Mean0.014610984
Median Absolute Deviation (MAD)0.005
Skewness0.20948107
Sum6.385
Variance3.7765751 × 10-5
MonotonicityNot monotonic
2023-11-18T00:13:58.922806image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.016 32
 
7.3%
0.012 28
 
6.4%
0.008 27
 
6.2%
0.015 26
 
5.9%
0.01 26
 
5.9%
0.017 25
 
5.7%
0.019 25
 
5.7%
0.009 24
 
5.5%
0.007 22
 
5.0%
0.018 20
 
4.6%
Other values (16) 182
41.6%
ValueCountFrequency (%)
0.003 4
 
0.9%
0.004 15
3.4%
0.005 6
 
1.4%
0.006 10
 
2.3%
0.007 22
5.0%
0.008 27
6.2%
0.009 24
5.5%
0.01 26
5.9%
0.011 19
4.3%
0.012 28
6.4%
ValueCountFrequency (%)
0.03 4
 
0.9%
0.028 7
 
1.6%
0.026 7
 
1.6%
0.025 6
 
1.4%
0.024 12
2.7%
0.023 13
3.0%
0.022 17
3.9%
0.021 13
3.0%
0.02 20
4.6%
0.019 25
5.7%

Ni
Real number (ℝ)

Distinct42
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51704805
Minimum0.01
Maximum2.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-11-18T00:13:59.174123image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.02
median0.06
Q30.46
95-th percentile2.64
Maximum2.78
Range2.77
Interquartile range (IQR)0.44

Descriptive statistics

Standard deviation0.85297576
Coefficient of variation (CV)1.6497031
Kurtosis0.5925459
Mean0.51704805
Median Absolute Deviation (MAD)0.04
Skewness1.4705522
Sum225.95
Variance0.72756764
MonotonicityNot monotonic
2023-11-18T00:13:59.498260image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0.02 70
16.0%
0.01 49
 
11.2%
0.03 38
 
8.7%
0.04 36
 
8.2%
0.06 26
 
5.9%
0.05 25
 
5.7%
0.07 19
 
4.3%
0.12 19
 
4.3%
0.08 16
 
3.7%
0.13 8
 
1.8%
Other values (32) 131
30.0%
ValueCountFrequency (%)
0.01 49
11.2%
0.02 70
16.0%
0.03 38
8.7%
0.04 36
8.2%
0.05 25
 
5.7%
0.06 26
 
5.9%
0.07 19
 
4.3%
0.08 16
 
3.7%
0.09 3
 
0.7%
0.1 6
 
1.4%
ValueCountFrequency (%)
2.78 3
0.7%
2.7 3
0.7%
2.67 6
1.4%
2.66 6
1.4%
2.65 3
0.7%
2.64 3
0.7%
2.63 6
1.4%
1.92 3
0.7%
1.9 3
0.7%
1.88 3
0.7%

Cr
Real number (ℝ)

Distinct65
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57045767
Minimum0.01
Maximum1.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-11-18T00:13:59.756529image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.02
Q10.12
median0.71
Q30.98
95-th percentile1.09
Maximum1.17
Range1.16
Interquartile range (IQR)0.86

Descriptive statistics

Standard deviation0.41176925
Coefficient of variation (CV)0.72182263
Kurtosis-1.6739411
Mean0.57045767
Median Absolute Deviation (MAD)0.36
Skewness-0.1332193
Sum249.29
Variance0.16955392
MonotonicityNot monotonic
2023-11-18T00:14:00.039809image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 31
 
7.1%
0.12 22
 
5.0%
0.02 20
 
4.6%
0.13 18
 
4.1%
1.09 13
 
3.0%
0.04 12
 
2.7%
0.99 12
 
2.7%
1.01 12
 
2.7%
1.06 12
 
2.7%
0.71 12
 
2.7%
Other values (55) 273
62.5%
ValueCountFrequency (%)
0.01 9
 
2.1%
0.02 20
4.6%
0.03 9
 
2.1%
0.04 12
 
2.7%
0.05 1
 
0.2%
0.06 3
 
0.7%
0.08 3
 
0.7%
0.09 12
 
2.7%
0.1 31
7.1%
0.11 6
 
1.4%
ValueCountFrequency (%)
1.17 4
 
0.9%
1.14 4
 
0.9%
1.12 3
 
0.7%
1.1 6
1.4%
1.09 13
3.0%
1.08 7
1.6%
1.07 6
1.4%
1.06 12
2.7%
1.05 6
1.4%
1.04 3
 
0.7%

Cu
Real number (ℝ)

Distinct21
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06778032
Minimum0.01
Maximum0.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-11-18T00:14:00.388839image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.02
median0.06
Q30.1
95-th percentile0.15
Maximum0.26
Range0.25
Interquartile range (IQR)0.08

Descriptive statistics

Standard deviation0.049161476
Coefficient of variation (CV)0.72530604
Kurtosis0.83231441
Mean0.06778032
Median Absolute Deviation (MAD)0.04
Skewness0.92793902
Sum29.62
Variance0.0024168507
MonotonicityNot monotonic
2023-11-18T00:14:00.662108image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.01 61
14.0%
0.02 61
14.0%
0.05 46
10.5%
0.07 37
8.5%
0.12 37
8.5%
0.08 26
 
5.9%
0.09 25
 
5.7%
0.06 23
 
5.3%
0.1 21
 
4.8%
0.04 21
 
4.8%
Other values (11) 79
18.1%
ValueCountFrequency (%)
0.01 61
14.0%
0.02 61
14.0%
0.03 17
 
3.9%
0.04 21
 
4.8%
0.05 46
10.5%
0.06 23
 
5.3%
0.07 37
8.5%
0.08 26
5.9%
0.09 25
5.7%
0.1 21
 
4.8%
ValueCountFrequency (%)
0.26 2
 
0.5%
0.22 3
 
0.7%
0.2 6
 
1.4%
0.19 3
 
0.7%
0.18 3
 
0.7%
0.16 4
 
0.9%
0.15 9
 
2.1%
0.14 4
 
0.9%
0.13 9
 
2.1%
0.12 37
8.5%

Mo
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06979405
Minimum0
Maximum0.24
Zeros250
Zeros (%)57.2%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-11-18T00:14:00.912474image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.17
95-th percentile0.22
Maximum0.24
Range0.24
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.088123681
Coefficient of variation (CV)1.2626245
Kurtosis-1.4792247
Mean0.06979405
Median Absolute Deviation (MAD)0
Skewness0.59000206
Sum30.5
Variance0.0077657832
MonotonicityNot monotonic
2023-11-18T00:14:01.341293image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 250
57.2%
0.17 38
 
8.7%
0.16 35
 
8.0%
0.18 22
 
5.0%
0.15 17
 
3.9%
0.19 16
 
3.7%
0.22 9
 
2.1%
0.24 9
 
2.1%
0.05 8
 
1.8%
0.21 7
 
1.6%
Other values (5) 26
 
5.9%
ValueCountFrequency (%)
0 250
57.2%
0.01 4
 
0.9%
0.02 4
 
0.9%
0.03 6
 
1.4%
0.05 8
 
1.8%
0.15 17
 
3.9%
0.16 35
 
8.0%
0.17 38
 
8.7%
0.18 22
 
5.0%
0.19 16
 
3.7%
ValueCountFrequency (%)
0.24 9
 
2.1%
0.23 6
 
1.4%
0.22 9
 
2.1%
0.21 7
 
1.6%
0.2 6
 
1.4%
0.19 16
3.7%
0.18 22
5.0%
0.17 38
8.7%
0.16 35
8.0%
0.15 17
3.9%

RedRatio
Real number (ℝ)

Distinct34
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean923.62929
Minimum240
Maximum5530
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-11-18T00:14:01.782114image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum240
5-th percentile420
Q1590
median740
Q31228
95-th percentile1740
Maximum5530
Range5290
Interquartile range (IQR)638

Descriptive statistics

Standard deviation576.61702
Coefficient of variation (CV)0.62429486
Kurtosis26.118385
Mean923.62929
Median Absolute Deviation (MAD)240
Skewness3.7937177
Sum403626
Variance332487.19
MonotonicityNot monotonic
2023-11-18T00:14:02.194012image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1740 60
13.7%
820 45
 
10.3%
610 38
 
8.7%
1270 30
 
6.9%
825 24
 
5.5%
1120 21
 
4.8%
640 21
 
4.8%
500 18
 
4.1%
420 18
 
4.1%
530 16
 
3.7%
Other values (24) 146
33.4%
ValueCountFrequency (%)
240 4
 
0.9%
289 2
 
0.5%
390 8
1.8%
420 18
4.1%
440 12
2.7%
460 4
 
0.9%
480 15
3.4%
500 18
4.1%
510 6
 
1.4%
530 16
3.7%
ValueCountFrequency (%)
5530 3
 
0.7%
1740 60
13.7%
1510 6
 
1.4%
1455 4
 
0.9%
1290 3
 
0.7%
1270 30
6.9%
1250 3
 
0.7%
1228 2
 
0.5%
1120 21
 
4.8%
1100 9
 
2.1%

dA
Real number (ℝ)

Distinct21
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.047180778
Minimum0
Maximum0.13
Zeros21
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-11-18T00:14:02.413468image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.008
Q10.02
median0.04
Q30.07
95-th percentile0.1
Maximum0.13
Range0.13
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.031093183
Coefficient of variation (CV)0.65902227
Kurtosis-0.5208966
Mean0.047180778
Median Absolute Deviation (MAD)0.02
Skewness0.49332602
Sum20.618
Variance0.00096678605
MonotonicityNot monotonic
2023-11-18T00:14:02.722636image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.03 56
12.8%
0.02 56
12.8%
0.06 48
11.0%
0.07 43
9.8%
0.08 42
9.6%
0.01 36
8.2%
0.04 31
7.1%
0.05 23
 
5.3%
0 21
 
4.8%
0.1 18
 
4.1%
Other values (11) 63
14.4%
ValueCountFrequency (%)
0 21
 
4.8%
0.008 4
 
0.9%
0.01 36
8.2%
0.013 4
 
0.9%
0.017 10
 
2.3%
0.02 56
12.8%
0.025 8
 
1.8%
0.03 56
12.8%
0.04 31
7.1%
0.046 4
 
0.9%
ValueCountFrequency (%)
0.13 6
 
1.4%
0.12 6
 
1.4%
0.11 5
 
1.1%
0.1 18
 
4.1%
0.09 12
 
2.7%
0.08 42
9.6%
0.07 43
9.8%
0.067 2
 
0.5%
0.06 48
11.0%
0.058 2
 
0.5%

dB
Real number (ℝ)

Distinct7
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0033913043
Minimum0
Maximum0.05
Zeros341
Zeros (%)78.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-11-18T00:14:02.929082image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.02
Maximum0.05
Range0.05
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0082398237
Coefficient of variation (CV)2.4296916
Kurtosis11.906066
Mean0.0033913043
Median Absolute Deviation (MAD)0
Skewness3.2672968
Sum1.482
Variance6.7894695 × 10-5
MonotonicityNot monotonic
2023-11-18T00:14:03.214318image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 341
78.0%
0.01 59
 
13.5%
0.02 13
 
3.0%
0.004 8
 
1.8%
0.03 7
 
1.6%
0.04 6
 
1.4%
0.05 3
 
0.7%
ValueCountFrequency (%)
0 341
78.0%
0.004 8
 
1.8%
0.01 59
 
13.5%
0.02 13
 
3.0%
0.03 7
 
1.6%
0.04 6
 
1.4%
0.05 3
 
0.7%
ValueCountFrequency (%)
0.05 3
 
0.7%
0.04 6
 
1.4%
0.03 7
 
1.6%
0.02 13
 
3.0%
0.01 59
 
13.5%
0.004 8
 
1.8%
0 341
78.0%

dC
Real number (ℝ)

Distinct10
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0077116705
Minimum0
Maximum0.058
Zeros219
Zeros (%)50.1%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-11-18T00:14:03.422011image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.01
95-th percentile0.03
Maximum0.058
Range0.058
Interquartile range (IQR)0.01

Descriptive statistics

Standard deviation0.010417618
Coefficient of variation (CV)1.3508899
Kurtosis3.4220982
Mean0.0077116705
Median Absolute Deviation (MAD)0
Skewness1.751796
Sum3.37
Variance0.00010852677
MonotonicityNot monotonic
2023-11-18T00:14:03.687301image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 219
50.1%
0.01 131
30.0%
0.02 34
 
7.8%
0.03 21
 
4.8%
0.04 12
 
2.7%
0.008 8
 
1.8%
0.004 6
 
1.4%
0.021 2
 
0.5%
0.012 2
 
0.5%
0.058 2
 
0.5%
ValueCountFrequency (%)
0 219
50.1%
0.004 6
 
1.4%
0.008 8
 
1.8%
0.01 131
30.0%
0.012 2
 
0.5%
0.02 34
 
7.8%
0.021 2
 
0.5%
0.03 21
 
4.8%
0.04 12
 
2.7%
0.058 2
 
0.5%
ValueCountFrequency (%)
0.058 2
 
0.5%
0.04 12
 
2.7%
0.03 21
 
4.8%
0.021 2
 
0.5%
0.02 34
 
7.8%
0.012 2
 
0.5%
0.01 131
30.0%
0.008 8
 
1.8%
0.004 6
 
1.4%
0 219
50.1%

Fatigue
Real number (ℝ)

Distinct250
Distinct (%)57.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean552.90389
Minimum225
Maximum1190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-11-18T00:14:04.050333image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum225
5-th percentile365
Q1448
median505
Q3578
95-th percentile1030.6
Maximum1190
Range965
Interquartile range (IQR)130

Descriptive statistics

Standard deviation186.63053
Coefficient of variation (CV)0.33754606
Kurtosis2.1165112
Mean552.90389
Median Absolute Deviation (MAD)65
Skewness1.5600748
Sum241619
Variance34830.954
MonotonicityNot monotonic
2023-11-18T00:14:04.524066image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
490 8
 
1.8%
550 7
 
1.6%
551 6
 
1.4%
502 6
 
1.4%
543 6
 
1.4%
415 6
 
1.4%
583 5
 
1.1%
482 5
 
1.1%
459 5
 
1.1%
483 5
 
1.1%
Other values (240) 378
86.5%
ValueCountFrequency (%)
225 1
0.2%
232 1
0.2%
235 2
0.5%
239 1
0.2%
241 1
0.2%
245 2
0.5%
255 2
0.5%
275 1
0.2%
325 2
0.5%
338 1
0.2%
ValueCountFrequency (%)
1190 1
0.2%
1144 1
0.2%
1139 1
0.2%
1124 1
0.2%
1120 1
0.2%
1110 1
0.2%
1104 1
0.2%
1089 1
0.2%
1086 1
0.2%
1082 1
0.2%

Interactions

2023-11-18T00:13:40.572889image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:11:41.896116image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:11:48.566312image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:11:53.583909image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:01.799939image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:08.583801image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:18.379609image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:25.766857image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:31.470658image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:37.791758image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:42.285741image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:48.527052image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:54.768365image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:59.563543image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:07.141285image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:11.793840image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:17.166476image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:23.269157image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:28.598905image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:34.396404image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:40.766373image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:11:42.177364image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:11:48.820601image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:11:53.829252image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:02.303594image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:08.882002image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:18.796494image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:26.006214image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:31.780830image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:38.022171image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:42.600899image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:48.804311image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:54.990773image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:59.805932image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:07.339753image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:12.026220image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:17.426781image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:23.491562image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:28.793385image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:34.766455image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:41.646020image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:11:42.818650image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:11:49.088885image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:11:54.145406image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:02.811237image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:09.279940image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:19.173488image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:26.262531image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:32.231625image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:38.223641image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:42.803358image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:49.140413image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:55.220157image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:00.173910image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:07.611025image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:12.255605image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:17.769862image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:23.887505image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:29.088597image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:35.122463image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:41.894373image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:11:43.112863image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:11:49.288352image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:11:54.584234image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:03.140356image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:09.557200image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:19.633260image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:26.563723image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:32.723311image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:38.491922image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:43.000861image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:49.478512image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:55.434582image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:00.502033image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:07.814483image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:12.503943image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:18.101974image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:24.277462image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:29.340924image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:35.473525image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:42.149673image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:11:43.400096image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:11:49.512752image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:11:54.824592image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:03.456510image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:09.857395image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:20.074079image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:26.744241image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:33.124241image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:38.679385image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:43.189353image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:49.810624image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:55.686909image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:00.934875image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:08.008960image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:12.712384image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:18.462012image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:24.579653image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:29.579285image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:35.744799image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:42.376071image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:11:43.740185image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:11:49.770068image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:11:55.159695image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-11-18T00:11:59.419304image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-11-18T00:12:16.063800image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:24.418460image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:29.812041image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:36.576007image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:41.189672image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:47.032050image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:53.162657image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:58.411656image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:05.649273image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:10.554154image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:15.498934image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:21.965642image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:27.407093image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:32.605194image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:39.335203image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:45.194532image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:11:47.431318image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:11:52.457918image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:11:59.775354image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:07.491720image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:16.529556image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:24.725639image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:30.112236image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:36.824344image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:41.392168image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:47.383112image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:53.555608image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:58.662951image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:06.009307image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:10.760637image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:15.790155image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:22.202011image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:27.618526image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:32.964235image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:39.572592image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:45.468798image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:11:47.717551image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:11:52.746148image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:00.186254image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:07.741053image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:17.015256image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:25.009878image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:30.419470image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:37.068691image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:41.661411image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:47.671343image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:53.923623image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:58.885355image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:06.351393image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:11.017916image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:16.163158image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:22.490240image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:27.908752image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:33.306321image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:39.833865image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:45.684223image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:11:48.000795image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:11:52.983513image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:00.730798image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:07.960466image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:17.458073image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:25.231286image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:30.797459image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:37.306092image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:41.866863image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:47.950594image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:54.253740image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:59.082860image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:06.626657image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:11.280214image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:16.481308image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:22.780464image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:28.122181image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:33.615492image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:40.071232image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:45.973454image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:11:48.308970image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:11:53.291693image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:01.340172image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:08.247699image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:17.899891image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:25.544451image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:31.169465image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:37.554399image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:42.079294image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:48.235832image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:54.502078image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:12:59.358092image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:06.871003image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:11.523564image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:16.850320image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:23.009852image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:28.386475image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:33.989493image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-18T00:13:40.286654image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-11-18T00:14:05.126456image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Sl. No.NTCtDTDtTTCSiMnPSNiCrCuMoRedRatiodAdBdCFatigueTHTTHtTHQCrCTQmTTtTCr
Sl. No.1.0000.6040.5400.5420.543-0.169-0.1820.2110.029-0.228-0.3580.6110.4110.3720.416-0.439-0.422-0.2380.0020.6890.8220.8650.8750.9470.6640.7440.744
NT0.6041.0000.5850.5850.585-0.280-0.7350.112-0.209-0.250-0.2800.5020.4960.2980.355-0.394-0.490-0.1370.0680.4700.8910.9940.9550.9940.6990.9950.995
Ct0.5400.5851.0000.9970.996-0.519-0.5390.142-0.039-0.1290.0550.1740.1950.1990.308-0.348-0.0850.057-0.1920.5400.4330.8840.6210.9950.8250.7010.701
DT0.5420.5850.9971.0000.999-0.519-0.5400.134-0.024-0.1200.0600.1640.2080.1960.303-0.351-0.0790.061-0.1920.5390.8850.8780.8880.9880.9990.9990.999
Dt0.5430.5850.9960.9991.000-0.519-0.5400.132-0.025-0.1200.0610.1610.2120.1930.302-0.350-0.0780.065-0.1900.5390.4310.8820.6190.9940.8010.6990.699
TT-0.169-0.280-0.519-0.519-0.5191.0000.286-0.1130.046-0.012-0.0700.055-0.019-0.061-0.0600.127-0.001-0.0730.075-0.5350.4940.9930.7020.9930.7360.9940.994
C-0.182-0.735-0.539-0.540-0.5400.2861.000-0.0620.3190.1030.025-0.225-0.233-0.148-0.2920.2810.199-0.111-0.008-0.1760.7220.9710.7850.9320.6520.7880.788
Si0.2110.1120.1420.1340.132-0.113-0.0621.000-0.100-0.007-0.2770.2590.2110.2140.1790.024-0.007-0.0540.0570.3130.0890.1460.1140.1710.1010.1030.103
Mn0.029-0.209-0.039-0.024-0.0250.0460.319-0.1001.0000.1530.055-0.287-0.082-0.037-0.1170.044-0.016-0.048-0.134-0.0940.2740.2870.2980.1890.0990.4040.404
P-0.228-0.250-0.129-0.120-0.120-0.0120.103-0.0070.1531.0000.226-0.380-0.071-0.258-0.0200.1890.2920.078-0.181-0.1010.2250.2010.2440.2520.1460.2110.211
S-0.358-0.2800.0550.0600.061-0.0700.025-0.2770.0550.2261.000-0.371-0.367-0.213-0.0780.0790.4850.236-0.241-0.2640.3340.2190.4150.2080.1070.1980.198
Ni0.6110.5020.1740.1640.1610.055-0.2250.259-0.287-0.380-0.3711.0000.3520.6330.346-0.531-0.367-0.2110.2950.4270.3260.4420.3810.5010.3480.3540.354
Cr0.4110.4960.1950.2080.212-0.019-0.2330.211-0.082-0.071-0.3670.3521.0000.3750.577-0.247-0.324-0.2460.1430.6330.6140.5910.6970.6800.4710.5010.501
Cu0.3720.2980.1990.1960.193-0.061-0.1480.214-0.037-0.258-0.2130.6330.3751.0000.366-0.493-0.161-0.1800.2880.3550.3330.3330.4560.3940.2600.2870.287
Mo0.4160.3550.3080.3030.302-0.060-0.2920.179-0.117-0.020-0.0780.3460.5770.3661.000-0.290-0.011-0.119-0.0810.5710.3590.3540.3770.4440.3030.3140.314
RedRatio-0.439-0.394-0.348-0.351-0.3500.1270.2810.0240.0440.1890.079-0.531-0.247-0.493-0.2901.0000.2890.058-0.168-0.2910.1550.2060.2120.2820.1910.2010.201
dA-0.422-0.490-0.085-0.079-0.078-0.0010.199-0.007-0.0160.2920.485-0.367-0.324-0.161-0.0110.2891.0000.235-0.278-0.2400.2870.1990.4550.1830.0800.2250.225
dB-0.238-0.1370.0570.0610.065-0.073-0.111-0.054-0.0480.0780.236-0.211-0.246-0.180-0.1190.0580.2351.000-0.140-0.2160.2280.0000.2930.0000.0000.2180.218
dC0.0020.068-0.192-0.192-0.1900.075-0.0080.057-0.134-0.181-0.2410.2950.1430.288-0.081-0.168-0.278-0.1401.0000.0060.2330.1640.3230.1480.0640.1940.194
Fatigue0.6890.4700.5400.5390.539-0.535-0.1760.313-0.094-0.101-0.2640.4270.6330.3550.571-0.291-0.240-0.2160.0061.0000.5690.9530.7810.9450.6670.9690.969
THT0.8220.8910.4330.8850.4310.4940.7220.0890.2740.2250.3340.3260.6140.3330.3590.1550.2870.2280.2330.5691.0000.9970.9000.8850.6230.7020.702
THt0.8650.9940.8840.8780.8820.9930.9710.1460.2870.2010.2190.4420.5910.3330.3540.2060.1990.0000.1640.9530.9971.0000.9990.8780.8880.9990.999
THQCr0.8750.9550.6210.8880.6190.7020.7850.1140.2980.2440.4150.3810.6970.4560.3770.2120.4550.2930.3230.7810.9000.9991.0000.8880.6260.7050.705
CT0.9470.9940.9950.9880.9940.9930.9320.1710.1890.2520.2080.5010.6800.3940.4440.2820.1830.0000.1480.9450.8850.8780.8881.0000.9990.9990.999
QmT0.6640.6990.8250.9990.8010.7360.6520.1010.0990.1460.1070.3480.4710.2600.3030.1910.0800.0000.0640.6670.6230.8880.6260.9991.0000.7050.705
Tt0.7440.9950.7010.9990.6990.9940.7880.1030.4040.2110.1980.3540.5010.2870.3140.2010.2250.2180.1940.9690.7020.9990.7050.9990.7051.0001.000
TCr0.7440.9950.7010.9990.6990.9940.7880.1030.4040.2110.1980.3540.5010.2870.3140.2010.2250.2180.1940.9690.7020.9990.7050.9990.7051.0001.000

Missing values

2023-11-18T00:13:46.517995image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-18T00:13:47.387283image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Sl. No.NTTHTTHtTHQCrCTCtDTDtQmTTTTtTCrCSiMnPSNiCrCuMoRedRatiodAdBdCFatigue
018853000300.030.00.0303000.00.260.210.440.0170.0220.010.020.010.08250.070.020.04232
128853000300.030.00.0303000.00.250.180.440.0090.0170.080.120.080.06100.110.000.04235
238853000300.030.00.0303000.00.260.270.430.0080.0150.020.030.010.012700.070.020.00235
348853000300.030.00.0303000.00.260.230.510.0180.0240.010.020.010.017400.060.000.00241
458853000300.030.00.0303000.00.220.190.420.0260.0220.010.020.020.08250.040.020.00225
568853000300.030.00.0303000.00.260.220.450.0260.0260.020.050.030.08250.060.020.02245
678853000300.030.00.0303000.00.280.270.500.0210.0180.020.030.010.012700.080.010.00255
788853000300.030.00.0303000.00.270.250.540.0200.0210.010.020.020.017400.070.000.00245
898853000300.030.00.0303000.00.270.230.370.0080.0300.060.100.090.06100.080.000.04275
9108853000300.030.00.0303000.00.220.260.470.0210.0230.020.020.010.012700.040.030.00239
Sl. No.NTTHTTHtTHQCrCTCtDTDtQmTTTTtTCrCSiMnPSNiCrCuMoRedRatiodAdBdCFatigue
427428930300093090.0881.80749.8601601200.50.180.250.790.0160.0150.071.080.080.155300.0170.0040.008858
4284299303000930340.2895.81270.2601601200.50.210.310.810.0120.0190.061.170.070.176900.0800.0000.0001144
4294309303000930340.2895.81270.2601601200.50.210.260.650.0150.0240.020.910.010.152400.0500.0100.0001028
4304319303000930340.2895.81270.2601601200.50.180.250.790.0160.0150.071.080.080.155300.0170.0040.008948
4314329303000930340.2895.81270.21401601200.50.210.310.810.0120.0190.061.170.070.176900.0800.0000.0001080
4324339303000930340.2895.81270.21401601200.50.210.260.650.0150.0240.020.910.010.152400.0500.0100.0001030
4334349303000930340.2895.81270.21401601200.50.180.250.790.0160.0150.071.080.080.155300.0170.0040.008957
4344359303000930340.2895.81270.2602001200.50.210.310.810.0120.0190.061.170.070.176900.0800.0000.0001104
4354369303000930340.2895.81270.2602001200.50.210.260.650.0150.0240.020.910.010.152400.0500.0100.0001008
4364379303000930340.2895.81270.2602001200.50.180.250.790.0160.0150.071.080.080.155300.0170.0040.008882